Exploration of the trait analysis for trait synchrony.

Parameters: environmental correction is TRUE.

Hypotheses

Check all hypotheses between traits and environmental drivers, and among traits

Big PCA

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Arthropods (above, herbivores)
SLA ++ D
  • disturbance favours fast-growing species
  • more nutrients allow for ‘cheap’ leaves
0.37 0.21 - 0.52 0.0000000 ✅ 0.40 0.11 0.09 0.35 0.2 - 0.5 ✅
Seed_mass – D
  • disturbance selects for smaller seeds (= colonisation)
Diaz et al. 2016 -0.31 -0.46 - -0.15 0.0004029 ✅ -0.24 -0.22 -0.14 -0.29 -0.45 - -0.14 ✅
LDMC – D Tougher = slow -0.37 -0.52 - -0.22 0.0000000 ✅ -0.41 -0.46 0.01 -0.36 -0.51 - -0.21 ✅
LeafN ++ D
  • more nutrients allow for ‘cheap’ leaves
Diaz et al. 2016 0.49 0.35 - 0.64 0.0000000 ✅ 0.43 0.48 0.11 0.48 0.34 - 0.62 ✅
Arthropods (above, omnicarnivores)
LeafP ++ D
  • more nutrients allow for ‘cheap’ leaves
0.54 0.41 - 0.68 0.0000000 ✅ 0.38 0.47 0.21 0.53 0.39 - 0.66 ✅
Root_tissue_density – D
  • conservative and/or ‘collaboration’ => if few nutrients, need for collaboration
Bergmann et al. 2020 -0.23 -0.39 - -0.08 0.0083783 ✅ -0.21 -0.02 -0.10 -0.27 -0.42 - -0.11 ✅
Arthropods (below, herbivores)
Aoc_BodySize – D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 0.04 -0.12 - 0.2 0.7210136 Inconclusive 0.04 -0.27 0.07 0.08 -0.08 - 0.25 Inconclusive
Aoc_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 0.33 0.18 - 0.48 0.0001446 ✅ 0.58 0.26 -0.17 0.32 0.17 - 0.48 ✅
Ah_BodySize +/- D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 -0.25 -0.41 - -0.09 0.0044538 B -0.32 -0.31 0.04 -0.26 -0.42 - -0.1 B
Arthropods (below, predators)
Ah_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 0.40 0.25 - 0.55 0.0000000 ✅ 0.32 0.38 0.13 0.28 0.13 - 0.44 ✅
Ah_Generalism ++ 0.37 0.22 - 0.53 0.0000000 ✅ 0.07 0.32 0.28 0.39 0.24 - 0.54 ✅
Bats
Ah_Generations ++ I 0.60 0.47 - 0.73 0.0000000 ✅ 0.69 0.38 0.08 0.54 0.4 - 0.68 ✅
Aoc_b_BodySize – D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 -0.04 -0.21 - 0.12 0.6770980 Inconclusive 0.05 -0.01 -0.04 -0.01 -0.17 - 0.15 Inconclusive
Aoc_b_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 0.11 -0.05 - 0.27 0.2443309 Inconclusive 0.11 0.04 0.03 0.11 -0.05 - 0.27 Inconclusive
Birds (insectivorous)
Ah_b_BodySize – D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 -0.30 -0.48 - -0.12 0.0040994 ✅ -0.35 -0.35 0.06 -0.25 -0.41 - -0.09 ✅
Ah_b_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 0.14 -0.05 - 0.33 0.2344673 Inconclusive 0.01 0.08 0.15 0.15 -0.01 - 0.32 Inconclusive
Ah_b_Generalism ++ -0.05 -0.24 - 0.14 0.6770980 Inconclusive 0.07 0.03 -0.17 -0.09 -0.28 - 0.1 Inconclusive
Bi_Size ++ more = fast 0.25 0.09 - 0.41 0.0044538 ✅ 0.34 0.19 0.00 0.31 0.16 - 0.47 ✅
Bi_Incub ++ more = disturbance 0.14 -0.03 - 0.3 0.1592976 Inconclusive 0.30 0.09 -0.08 0.18 0.02 - 0.34 ✔
Butterflies
Bi_TOffsprings – I Small = fast -0.12 -0.28 - 0.05 0.2356609 Inconclusive -0.32 -0.09 0.11 -0.15 -0.31 - 0.01 Inconclusive
Bi_GenLength – 0.21 0.05 - 0.37 0.0190256 ❌ 0.33 0.17 -0.04 0.27 0.11 - 0.42 ❌
Bi_AgeMax – I Small = fast 0.26 0.1 - 0.42 0.0042300 ❌ 0.31 0.21 0.01 0.30 0.15 - 0.46 ❌
but_flight ++ Early emergence favorable bc disturbance starts early B<9a>rschig et al. 2013 0.33 0.18 - 0.49 0.0001446 ✅ 0.66 0.27 -0.15 0.25 0.09 - 0.41 ✅
but_GenYear ++ High reproduction can compensate mortality due to disturbance B<9a>rschig et al. 2013 0.20 0.04 - 0.36 0.0302889 ✅ 0.09 0.10 0.11 0.23 0.07 - 0.39 ✅
Collembola
but_hibernation ++ Later hibernation stage means butterflies ready before disturbance 0.31 0.15 - 0.47 0.0004387 ✅ 0.53 0.23 -0.07 0.25 0.09 - 0.41 ✅
but_Size ++ Larger wings = more dispersal B<9a>rschig et al. 2013 -0.08 -0.24 - 0.08 0.4278651 Inconclusive -0.02 -0.03 -0.06 -0.13 -0.29 - 0.03 Inconclusive
but_Generalism ++ High nutrients = low plant diversity, hence more generalists 0.28 0.12 - 0.44 0.0017418 ✅ 0.09 0.06 0.22 0.28 0.12 - 0.44 ✅
col_Sex – 0.02 -0.15 - 0.19 0.8594972 Inconclusive -0.07 0.05 0.06 0.01 -0.16 - 0.17 Inconclusive
Microbes
col_Gen_per_Year ++ Furca = escape disturbance -0.09 -0.26 - 0.07 0.3568083 Inconclusive 0.27 -0.08 -0.30 -0.09 -0.26 - 0.08 Inconclusive
col_Depth ++ -0.03 -0.19 - 0.14 0.8059142 Inconclusive 0.07 -0.10 -0.06 -0.01 -0.18 - 0.16 Inconclusive
col_Size – a/ more resources or b/ selected 0.03 -0.13 - 0.2 0.7483165 Inconclusive -0.27 0.01 0.24 0.04 -0.13 - 0.21 Inconclusive
mites_DaysAdult – longer lifespan = slow -0.06 -0.23 - 0.11 0.5561305 Inconclusive 0.01 -0.13 -0.03 -0.05 -0.22 - 0.12 Inconclusive
mites_Mass – Mass = size = slow -0.20 -0.37 - -0.04 0.0302889 ✅ 0.00 -0.17 -0.19 -0.16 -0.32 - 0.01 Inconclusive
Mites
mites_Sex – I Habitat openness = sexual repro, more LUI = more growth 0.00 -0.17 - 0.17 0.9682000 Inconclusive 0.18 -0.01 -0.13 0.01 -0.16 - 0.18 Inconclusive
mites_Hab_spec ++ more disturbance = more in the soil -0.07 -0.24 - 0.1 0.5204879 Inconclusive -0.18 -0.13 0.08 -0.03 -0.2 - 0.14 Inconclusive
mites_Feed_spec ++ 0.12 -0.05 - 0.28 0.2443309 Inconclusive 0.12 0.06 0.02 0.12 -0.04 - 0.29 Inconclusive
P_patho ++ More bacteria = more bacterivores 0.49 0.35 - 0.63 0.0000000 ✅ 0.34 0.36 0.20 0.46 0.32 - 0.61 ✅
Pb_Size – a/ More nutrients = more resources to grow. b/ Disturbance selects smaller size -0.24 -0.4 - -0.08 0.0073918 ✅ -0.29 -0.10 -0.05 -0.27 -0.43 - -0.11 ✅
Plants (AG)
Ps_Size – a/ More nutrients = more resources to grow. b/ Disturbance selects smaller size -0.45 -0.59 - -0.3 0.0000000 ✅ -0.12 -0.35 -0.33 -0.31 -0.46 - -0.15 ✅
mic_FB – D and I Fungi slower than bacteria, linked to slow plant traits Fungi dominate in low-nutrient soils; associated with slow plants de Vries et al. 2006, de Vries et al. 2012, Boeddinghaus et al. 2019 -0.31 -0.46 - -0.15 0.0004406 ✅ -0.33 -0.31 -0.04 -0.24 -0.4 - -0.08 ✅
mic_Fpathotroph ++ Fast= more parasites check https://www.nature.com/articles/s41467-021-23605-y Fast plant invest less in defenses, so more pathogens 0.40 0.25 - 0.55 0.0000000 ✅ 0.23 0.46 0.16 0.31 0.15 - 0.46 ✅
mic_O.C.ratio –
  • more oligo (Acido) than copio (Actibo, alphapto) in slow
Check Neff 2015, Ramirez 2010, Fierer 2012 -0.19 -0.35 - -0.03 0.0327321 ✅ 0.06 -0.15 -0.19 -0.04 -0.2 - 0.12 Inconclusive
mic_Bvolume +/- D a/ small cells more efficient for diffusive uptake (-) OR b/ for a given substrate demand, large radius compensates low substrate concentrations Westoby et al. 2021 -0.37 -0.52 - -0.22 0.0000000 B -0.04 -0.16 -0.33 -0.31 -0.46 - -0.15 B
Plants (BG)
mic_Bgenome_size – D
  • Larger genomes should be more successful in resource-poor environments v. S strategies have smaller genomes (Westoby et al.)
Leff et al. 2015; Konstantinidis (2004) -0.23 -0.39 - -0.07 0.0094979 ✅ -0.29 -0.41 0.09 -0.04 -0.21 - 0.12 Inconclusive
Protists
bat_mass – Large more sensitive to disturbance BUT large less sensitive to urbanisation?? Farneda et al. 2015, Moir et al. 2021 , Jung et al 2018 -0.12 -0.28 - 0.05 0.2356609 Inconclusive -0.05 -0.03 -0.10 -0.17 -0.33 - -0.01 ✔
Protists bacterivores
bat_lifespan 0.02 -0.14 - 0.19 0.8059142 Inconclusive -0.01 -0.15 0.06 0.06 -0.1 - 0.23 Inconclusive
Protists predators
bat_offspring ++ opposite to size -0.11 -0.27 - 0.05 0.2492746 Inconclusive 0.02 0.14 -0.17 -0.16 -0.32 - 0.01 Inconclusive

Identification of strategy axes for each group

Plants, above- and below-ground

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Plants (AG)
SLA ++ D
  • disturbance favours fast-growing species
  • more nutrients allow for ‘cheap’ leaves
0.37 0.21 - 0.52 0.0000000 ✅ 0.40 0.11 0.09 0.35 0.2 - 0.5 ✅
Seed_mass – D
  • disturbance selects for smaller seeds (= colonisation)
Diaz et al. 2016 -0.31 -0.46 - -0.15 0.0004029 ✅ -0.24 -0.22 -0.14 -0.29 -0.45 - -0.14 ✅
LDMC – D Tougher = slow -0.37 -0.52 - -0.22 0.0000000 ✅ -0.41 -0.46 0.01 -0.36 -0.51 - -0.21 ✅
LeafN ++ D
  • more nutrients allow for ‘cheap’ leaves
Diaz et al. 2016 0.49 0.35 - 0.64 0.0000000 ✅ 0.43 0.48 0.11 0.48 0.34 - 0.62 ✅
LeafP ++ D
  • more nutrients allow for ‘cheap’ leaves
0.54 0.41 - 0.68 0.0000000 ✅ 0.38 0.47 0.21 0.53 0.39 - 0.66 ✅
Plants (BG)
Root_tissue_density – D
  • conservative and/or ‘collaboration’ => if few nutrients, need for collaboration
Bergmann et al. 2020 -0.23 -0.39 - -0.08 0.0083783 ✅ -0.21 -0.02 -0.10 -0.27 -0.42 - -0.11 ✅ 
## [1] "Plants, All"

Bacteria & fungi

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Microbes
mic_FB – D and I Fungi slower than bacteria, linked to slow plant traits Fungi dominate in low-nutrient soils; associated with slow plants de Vries et al. 2006, de Vries et al. 2012, Boeddinghaus et al. 2019 -0.31 -0.46 - -0.15 0.0004406 ✅ -0.33 -0.31 -0.04 -0.24 -0.4 - -0.08 ✅
mic_Fpathotroph ++ Fast= more parasites check https://www.nature.com/articles/s41467-021-23605-y Fast plant invest less in defenses, so more pathogens 0.40 0.25 - 0.55 0.0000000 ✅ 0.23 0.46 0.16 0.31 0.15 - 0.46 ✅
mic_O.C.ratio –
  • more oligo (Acido) than copio (Actibo, alphapto) in slow
Check Neff 2015, Ramirez 2010, Fierer 2012 -0.19 -0.35 - -0.03 0.0327321 ✅ 0.06 -0.15 -0.19 -0.04 -0.2 - 0.12 Inconclusive
mic_Bvolume +/- D a/ small cells more efficient for diffusive uptake (-) OR b/ for a given substrate demand, large radius compensates low substrate concentrations Westoby et al. 2021 -0.37 -0.52 - -0.22 0.0000000 B -0.04 -0.16 -0.33 -0.31 -0.46 - -0.15 B
mic_Bgenome_size – D
  • Larger genomes should be more successful in resource-poor environments v. S strategies have smaller genomes (Westoby et al.)
Leff et al. 2015; Konstantinidis (2004) -0.23 -0.39 - -0.07 0.0094979 ✅ -0.29 -0.41 0.09 -0.04 -0.21 - 0.12 Inconclusive

Arthropods, above-ground

Herbivores

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Arthropods (above, herbivores)
Aoc_BodySize – D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 0.04 -0.12 - 0.2 0.7210136 Inconclusive 0.04 -0.27 0.07 0.08 -0.08 - 0.25 Inconclusive
Aoc_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 0.33 0.18 - 0.48 0.0001446 ✅ 0.58 0.26 -0.17 0.32 0.17 - 0.48 ✅
Ah_BodySize +/- D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 -0.25 -0.41 - -0.09 0.0044538 B -0.32 -0.31 0.04 -0.26 -0.42 - -0.1 B
Ah_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 0.40 0.25 - 0.55 0.0000000 ✅ 0.32 0.38 0.13 0.28 0.13 - 0.44 ✅
Arthropods (above, omnicarnivores)
Ah_Generalism ++ 0.37 0.22 - 0.53 0.0000000 ✅ 0.07 0.32 0.28 0.39 0.24 - 0.54 ✅
Ah_Generations ++ I 0.60 0.47 - 0.73 0.0000000 ✅ 0.69 0.38 0.08 0.54 0.4 - 0.68 ✅
Arthropods (below, herbivores)
Aoc_b_BodySize – D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 -0.04 -0.21 - 0.12 0.6770980 Inconclusive 0.05 -0.01 -0.04 -0.01 -0.17 - 0.15 Inconclusive
Aoc_b_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 0.11 -0.05 - 0.27 0.2443309 Inconclusive 0.11 0.04 0.03 0.11 -0.05 - 0.27 Inconclusive
Ah_b_BodySize – D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 -0.30 -0.48 - -0.12 0.0040994 ✅ -0.35 -0.35 0.06 -0.25 -0.41 - -0.09 ✅
Arthropods (below, predators)
Ah_b_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 0.14 -0.05 - 0.33 0.2344673 Inconclusive 0.01 0.08 0.15 0.15 -0.01 - 0.32 Inconclusive
Ah_b_Generalism ++ -0.05 -0.24 - 0.14 0.6770980 Inconclusive 0.07 0.03 -0.17 -0.09 -0.28 - 0.1 Inconclusive
Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Butterflies
but_flight ++ Early emergence favorable bc disturbance starts early B<9a>rschig et al. 2013 0.33 0.18 - 0.49 0.0001446 ✅ 0.66 0.27 -0.15 0.25 0.09 - 0.41 ✅
but_GenYear ++ High reproduction can compensate mortality due to disturbance B<9a>rschig et al. 2013 0.20 0.04 - 0.36 0.0302889 ✅ 0.09 0.10 0.11 0.23 0.07 - 0.39 ✅
but_hibernation ++ Later hibernation stage means butterflies ready before disturbance 0.31 0.15 - 0.47 0.0004387 ✅ 0.53 0.23 -0.07 0.25 0.09 - 0.41 ✅
but_Size ++ Larger wings = more dispersal B<9a>rschig et al. 2013 -0.08 -0.24 - 0.08 0.4278651 Inconclusive -0.02 -0.03 -0.06 -0.13 -0.29 - 0.03 Inconclusive
but_Generalism ++ High nutrients = low plant diversity, hence more generalists 0.28 0.12 - 0.44 0.0017418 ✅ 0.09 0.06 0.22 0.28 0.12 - 0.44 ✅

Predators

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Arthropods (above, omnicarnivores)
Aoc_BodySize – D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 0.04 -0.12 - 0.2 0.7210136 Inconclusive 0.04 -0.27 0.07 0.08 -0.08 - 0.25 Inconclusive
Aoc_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 0.33 0.18 - 0.48 0.0001446 ✅ 0.58 0.26 -0.17 0.32 0.17 - 0.48 ✅
Arthropods (below, predators)
Aoc_b_BodySize – D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 -0.04 -0.21 - 0.12 0.6770980 Inconclusive 0.05 -0.01 -0.04 -0.01 -0.17 - 0.15 Inconclusive
Aoc_b_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 0.11 -0.05 - 0.27 0.2443309 Inconclusive 0.11 0.04 0.03 0.11 -0.05 - 0.27 Inconclusive

Protists

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Protists
P_patho ++ More bacteria = more bacterivores 0.49 0.35 - 0.63 0.0000000 ✅ 0.34 0.36 0.20 0.46 0.32 - 0.61 ✅
Protists bacterivores
Pb_Size – a/ More nutrients = more resources to grow. b/ Disturbance selects smaller size -0.24 -0.4 - -0.08 0.0073918 ✅ -0.29 -0.10 -0.05 -0.27 -0.43 - -0.11 ✅
Protists predators
Ps_Size – a/ More nutrients = more resources to grow. b/ Disturbance selects smaller size -0.45 -0.59 - -0.3 0.0000000 ✅ -0.12 -0.35 -0.33 -0.31 -0.46 - -0.15 ✅

Birds

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Birds (insectivorous)
Bi_Size ++ more = fast 0.25 0.09 - 0.41 0.0044538 ✅ 0.34 0.19 0.00 0.31 0.16 - 0.47 ✅
Bi_Incub ++ more = disturbance 0.14 -0.03 - 0.3 0.1592976 Inconclusive 0.30 0.09 -0.08 0.18 0.02 - 0.34 ✔
Bi_TOffsprings – I Small = fast -0.12 -0.28 - 0.05 0.2356609 Inconclusive -0.32 -0.09 0.11 -0.15 -0.31 - 0.01 Inconclusive
Bi_GenLength – 0.21 0.05 - 0.37 0.0190256 ❌ 0.33 0.17 -0.04 0.27 0.11 - 0.42 ❌
Bi_AgeMax – I Small = fast 0.26 0.1 - 0.42 0.0042300 ❌ 0.31 0.21 0.01 0.30 0.15 - 0.46 ❌

Mites & collembola

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Mites
mites_DaysAdult – longer lifespan = slow -0.06 -0.23 - 0.11 0.5561305 Inconclusive 0.01 -0.13 -0.03 -0.05 -0.22 - 0.12 Inconclusive
mites_Mass – Mass = size = slow -0.20 -0.37 - -0.04 0.0302889 ✅ 0.00 -0.17 -0.19 -0.16 -0.32 - 0.01 Inconclusive
mites_Sex – I Habitat openness = sexual repro, more LUI = more growth 0.00 -0.17 - 0.17 0.9682000 Inconclusive 0.18 -0.01 -0.13 0.01 -0.16 - 0.18 Inconclusive
mites_Hab_spec ++ more disturbance = more in the soil -0.07 -0.24 - 0.1 0.5204879 Inconclusive -0.18 -0.13 0.08 -0.03 -0.2 - 0.14 Inconclusive
mites_Feed_spec ++ 0.12 -0.05 - 0.28 0.2443309 Inconclusive 0.12 0.06 0.02 0.12 -0.04 - 0.29 Inconclusive 
## NULL

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Collembola
col_Sex – 0.02 -0.15 - 0.19 0.8594972 Inconclusive -0.07 0.05 0.06 0.01 -0.16 - 0.17 Inconclusive
col_Gen_per_Year ++ Furca = escape disturbance -0.09 -0.26 - 0.07 0.3568083 Inconclusive 0.27 -0.08 -0.30 -0.09 -0.26 - 0.08 Inconclusive
col_Depth ++ -0.03 -0.19 - 0.14 0.8059142 Inconclusive 0.07 -0.10 -0.06 -0.01 -0.18 - 0.16 Inconclusive
col_Size – a/ more resources or b/ selected 0.03 -0.13 - 0.2 0.7483165 Inconclusive -0.27 0.01 0.24 0.04 -0.13 - 0.21 Inconclusive 
## NULL

Bats

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Bats
bat_mass – Large more sensitive to disturbance BUT large less sensitive to urbanisation?? Farneda et al. 2015, Moir et al. 2021 , Jung et al 2018 -0.12 -0.28 - 0.05 0.2356609 Inconclusive -0.05 -0.03 -0.10 -0.17 -0.33 - -0.01 ✔
bat_lifespan 0.02 -0.14 - 0.19 0.8059142 Inconclusive -0.01 -0.15 0.06 0.06 -0.1 - 0.23 Inconclusive
bat_offspring ++ opposite to size -0.11 -0.27 - 0.05 0.2492746 Inconclusive 0.02 0.14 -0.17 -0.16 -0.32 - 0.01 Inconclusive 
## NULL

Try to do a SEM

## 
##  Pearson's product-moment correlation
## 
## data:  PCA_pca$ind$coord[, 1] and env_data_lui[Plot %in% dd$Plot, ]$LUI
## t = 11.425, df = 148, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.588920 0.761309
## sample estimates:
##       cor 
## 0.6845709

Run above-ground model, simple

## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
##            cfi          rmsea rmsea.ci.upper            bic 
##          0.992          0.100          0.264       2446.850
## quartz_off_screen 
##                 2

Run below-ground model, simple

## lavaan 0.6-12 ended normally after 1 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        40
## 
##                                                   Used       Total
##   Number of observations                           120         150
## 
## Model Test User Model:
##                                                       
##   Test statistic                                10.041
##   Degrees of freedom                                 4
##   P-value (Chi-square)                           0.040
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                          Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   plant ~                                                                     
##     LUI                     0.577    0.077    7.506    0.000    0.577    0.565
##   Protists_patho ~                                                            
##     LUI                     0.327    0.093    3.525    0.000    0.327    0.320
##     plant                   0.326    0.091    3.585    0.000    0.326    0.326
##   Microbes ~                                                                  
##     LUI                     0.204    0.083    2.466    0.014    0.204    0.212
##     plant                   0.454    0.081    5.614    0.000    0.454    0.483
##   Protists_bact ~                                                             
##     LUI                     0.258    0.109    2.361    0.018    0.258    0.268
##     plant                   0.093    0.116    0.796    0.426    0.093    0.098
##     Microbes               -0.237    0.112   -2.112    0.035   -0.237   -0.236
##     Protists_patho          0.053    0.100    0.528    0.597    0.053    0.056
##   Protists_sec ~                                                              
##     LUI                     0.251    0.098    2.574    0.010    0.251    0.272
##     Protists_bact          -0.020    0.080   -0.247    0.805   -0.020   -0.021
##     plant                   0.092    0.102    0.898    0.369    0.092    0.101
##     Microbes                0.193    0.100    1.928    0.054    0.193    0.200
##     Protists_patho          0.020    0.087    0.228    0.819    0.020    0.022
##   Mites ~                                                                     
##     LUI                     0.057    0.115    0.493    0.622    0.057    0.059
##     plant                   0.182    0.118    1.548    0.122    0.182    0.192
##     Microbes                0.189    0.117    1.619    0.105    0.189    0.187
##     Protists_sec           -0.156    0.105   -1.483    0.138   -0.156   -0.148
##     Protists_bact           0.078    0.092    0.854    0.393    0.078    0.078
##     Protists_patho         -0.176    0.101   -1.747    0.081   -0.176   -0.185
##   Coll ~                                                                      
##     LUI                     0.020    0.120    0.170    0.865    0.020    0.021
##     Microbes               -0.217    0.121   -1.790    0.073   -0.217   -0.213
##     plant                   0.094    0.122    0.772    0.440    0.094    0.099
##     Protists_sec            0.071    0.109    0.650    0.516    0.071    0.067
##     Protists_bact          -0.122    0.095   -1.278    0.201   -0.122   -0.120
##     Protists_patho          0.001    0.104    0.013    0.989    0.001    0.001
##   Arth_omnicarni_below ~                                                      
##     LUI                     0.113    0.109    1.036    0.300    0.113    0.115
##     Mites                   0.243    0.088    2.748    0.006    0.243    0.240
##     Coll                   -0.234    0.085   -2.763    0.006   -0.234   -0.233
##     Protists_sec           -0.165    0.102   -1.623    0.105   -0.165   -0.155
##     plant                  -0.021    0.107   -0.197    0.844   -0.021   -0.022
##     Protists_patho          0.067    0.100    0.670    0.503    0.067    0.069
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .plant             0.756    0.098    7.746    0.000    0.756    0.681
##    .Protists_patho    0.750    0.097    7.746    0.000    0.750    0.674
##    .Microbes          0.594    0.077    7.746    0.000    0.594    0.605
##    .Protists_bact     0.898    0.116    7.746    0.000    0.898    0.908
##    .Protists_sec      0.686    0.089    7.746    0.000    0.686    0.757
##    .Mites             0.908    0.117    7.746    0.000    0.908    0.904
##    .Coll              0.975    0.126    7.746    0.000    0.975    0.965
##    .Arth_mncrn_blw    0.871    0.112    7.746    0.000    0.871    0.848
## lavaan 0.6-12 ended normally after 10 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        36
## 
##                                                   Used       Total
##   Number of observations                           120         150
## 
## Model Test User Model:
##                                                       
##   Test statistic                                11.936
##   Degrees of freedom                                 8
##   P-value (Chi-square)                           0.154
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                          Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   plant ~                                                                     
##     LUI                     0.577    0.077    7.506    0.000    0.577    0.565
##   Protists_patho ~                                                            
##     LUI                     0.327    0.093    3.527    0.000    0.327    0.320
##     plant                   0.326    0.091    3.591    0.000    0.326    0.326
##   Microbes ~                                                                  
##     LUI                     0.204    0.083    2.466    0.014    0.204    0.212
##     plant                   0.454    0.081    5.614    0.000    0.454    0.483
##   Protists_bact ~                                                             
##     LUI                     0.272    0.104    2.608    0.009    0.272    0.283
##     plant                   0.103    0.112    0.919    0.358    0.103    0.109
##     Microbes               -0.222    0.112   -1.972    0.049   -0.222   -0.221
##   Protists_sec ~                                                              
##     LUI                     0.256    0.094    2.739    0.006    0.256    0.278
##     plant                   0.095    0.098    0.972    0.331    0.095    0.106
##     Microbes                0.199    0.100    1.992    0.046    0.199    0.207
##     Protists_bact          -0.019    0.080   -0.237    0.813   -0.019   -0.020
##   Mites ~                                                                     
##     LUI                     0.013    0.112    0.113    0.910    0.013    0.013
##     plant                   0.150    0.115    1.306    0.191    0.150    0.158
##     Microbes                0.136    0.118    1.156    0.248    0.136    0.135
##     Protists_sec           -0.159    0.106   -1.500    0.134   -0.159   -0.152
##     Protists_bact           0.071    0.093    0.767    0.443    0.071    0.071
##   Coll ~                                                                      
##     LUI                     0.021    0.115    0.180    0.857    0.021    0.021
##     plant                   0.094    0.117    0.805    0.421    0.094    0.099
##     Microbes               -0.216    0.121   -1.789    0.074   -0.216   -0.213
##     Protists_bact          -0.122    0.095   -1.279    0.201   -0.122   -0.120
##     Protists_sec            0.071    0.109    0.650    0.515    0.071    0.067
##   Arth_omnicarni_below ~                                                      
##     LUI                     0.112    0.095    1.181    0.238    0.112    0.114
##     Mites                   0.238    0.086    2.770    0.006    0.238    0.234
##     Coll                   -0.226    0.085   -2.672    0.008   -0.226   -0.224
##     Protists_sec           -0.160    0.099   -1.614    0.107   -0.160   -0.150
##     Protists_bact           0.098    0.088    1.109    0.267    0.098    0.096
## 
## Covariances:
##                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .Protists_patho ~~                                                      
##    .Arth_mncrn_blw     0.050    0.074    0.675    0.500    0.050    0.062
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .plant             0.756    0.098    7.746    0.000    0.756    0.681
##    .Protists_patho    0.750    0.097    7.746    0.000    0.750    0.674
##    .Microbes          0.594    0.077    7.746    0.000    0.594    0.605
##    .Protists_bact     0.900    0.116    7.746    0.000    0.900    0.914
##    .Protists_sec      0.686    0.089    7.746    0.000    0.686    0.757
##    .Mites             0.930    0.120    7.746    0.000    0.930    0.933
##    .Coll              0.975    0.126    7.746    0.000    0.975    0.964
##    .Arth_mncrn_blw    0.865    0.112    7.746    0.000    0.865    0.840
##            cfi          rmsea rmsea.ci.upper            bic 
##          0.981          0.064          0.135       2678.765
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
##            cfi          rmsea rmsea.ci.upper            bic 
##          1.000          0.000          0.061       3233.978
## quartz_off_screen 
##                 2

Use the parameters defined in the simple SEM

## 
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
## 
##     discard
## The following object is masked from 'package:readr':
## 
##     col_factor
## The following object is masked from 'package:viridis':
## 
##     viridis_pal
## Scale for 'size' is already present. Adding another scale for 'size', which
## will replace the existing scale.
## 
## Attaching package: 'fishmethods'
## The following object is masked from 'package:lavaan':
## 
##     growth
## [1] 0.27191368 0.06730893

get multidiv

## `set.seed(1)` was used to initialize repeatable random subsampling.
## Please record this for your records so others can reproduce.
## Try `set.seed(1); .Random.seed` for the full vector
## ...
## 50OTUs were removed because they are no longer 
## present in any sample after random subsampling
## ...
## `set.seed(1)` was used to initialize repeatable random subsampling.
## Please record this for your records so others can reproduce.
## Try `set.seed(1); .Random.seed` for the full vector
## ...
## 11288OTUs were removed because they are no longer 
## present in any sample after random subsampling
## ...

Check effect on EF MF

## [1] 496
## [1] 496
## quartz_off_screen 
##                 2
## quartz_off_screen 
##                 2
## 
##  Pearson's product-moment correlation
## 
## data:  Dim.1_all and Dim.1_fun
## t = 8.4277, df = 148, p-value = 2.867e-14
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4502908 0.6686930
## sample estimates:
##       cor 
## 0.5694578
## Start:  AIC=-1.43
## Dim.1_fun ~ Dim.1_all
## 
##             Df Sum of Sq    RSS    AIC
## <none>                   144.67 -1.430
## - Dim.1_all  1    69.427 214.09 55.367
## Start:  AIC=20.06
## Dim.1_fun ~ Dim.1_mic
## 
##             Df Sum of Sq    RSS    AIC
## <none>                   166.95 20.062
## - Dim.1_mic  1     47.14 214.09 55.367
## Start:  AIC=28.79
## Dim.1_fun ~ Dim.1_plants
## 
##                Df Sum of Sq    RSS    AIC
## <none>                      176.96 28.792
## - Dim.1_plants  1    37.135 214.09 55.367
## Start:  AIC=37.22
## Dim.1_fun ~ multidiv
## 
##            Df Sum of Sq    RSS    AIC
## <none>                  187.18 37.218
## - multidiv  1    26.911 214.09 55.367
## Start:  AIC=16.15
## Dim.1_fun ~ LUI
## 
##        Df Sum of Sq    RSS    AIC
## <none>              162.65 16.146
## - LUI   1    51.442 214.09 55.367
##                                                Model Estimate (sd)
## 1   Functions slow-fast ~ entire community slow-fast   0.38 (0.04)
## 2             Functions slow-fast ~ plants slow-fast   0.28 (0.05)
## 3 Functions slow-fast ~ bacteria and fungi slow-fast  -0.41 (0.06)
## 4                          Functions slow-fast ~ LUI   0.59 (0.09)
## 5     Functions slow-fast ~ taxonomic multidiversity  -0.42 (0.09)
##                   Pval   R2        adj.P
## 1 2.86689601345489e-14 0.32 1.433448e-13
## 2 1.15203552327312e-07 0.17 1.440044e-07
## 3 1.38787514334138e-09 0.21 2.313125e-09
## 4 1.92923225172451e-10 0.24 4.823081e-10
## 5  8.5349790028157e-06 0.12 8.534979e-06
## lavaan 0.6-12 ended normally after 1 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         5
## 
##   Number of observations                           150
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws            1000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   Dim.1_fun ~                                         
##     LUI        (d)    0.226    0.096    2.362    0.018
##     Dim.1_all  (a)    0.290    0.058    4.986    0.000
##   Dim.1_all ~                                         
##     LUI        (b)    1.245    0.119   10.497    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .Dim.1_fun         0.937    0.109    8.568    0.000
##    .Dim.1_all         1.746    0.197    8.841    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)
##     indirect          0.361    0.083    4.370    0.000
##     total             0.588    0.074    7.898    0.000
##     diff              0.135    0.163    0.828    0.408
## quartz_off_screen 
##                 2